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Objective:The objective was to evaluate the efficacy of the Revised National Institute for Occupational Safety and Health (NIOSH) lifting equation (RNLE) to predict risk of low-back pain (LBP).Background:In 1993, NIOSH published the RNLE as a risk assessment method for LBP associated with manual lifting. To date, there has been little research evaluating the RNLE as a predictor of the risk of LBP using a prospective design.Methods:A total of 78 healthy industrial workers' baseline LBP risk exposures and self-reported LBP at one-year follow-up were investigated. The composite lifting index (CLI), the outcome measure of the RNLE for analyzing multiple lifting tasks, was used as the main risk predictor. The risk was estimated using the mean and maximum CLI variables at baseline and self-reported LBP during the follow-up. Odds ratios (ORs) were calculated using a logistic regression analysis adjusted for covariates that included personal factors, physical activities outside of work, job factors, and work-related psychosocial characteristics.Results:The one-year self-reported LBP incidence was 32.1%. After controlling for history of prior LBP, supervisory support, and job strain, the categorical mean and maximum CLI above 2 had a significant relationship (OR = 5.1–6.5) with self-reported LBP, as compared with the CLI below or equal to 1. The correlation between the continuous CLI variables and LBP was unclear.Conclusions:The CLI > 2 threshold may be useful for predicting self-reported LBP. Research with a larger sample size is needed to clarify the exposure–response relationship between the CLI and LBP.

Objective:The objective of this article is to evaluate the impact of the Revised National Institute for Occupational Safety and Health Lifting Equation (RNLE).Background:The RNLE has been used extensively as a risk assessment method for prevention of low back pain (LBP). However, the impact of the RNLE has not been documented.Methods:A systematic review of the literature on the RNLE was conducted. The review consisted of three parts: characterization of the RNLE publications, assessment of the impact of the RNLE, and evaluation of the influences of the RNLE on ergonomic standards. The literature for assessing the impact was categorized into four research areas: methodology, laboratory, field, and risk assessment studies using the Lifting Index (LI) or Composite LI (CLI), both of which are the products of the RNLE.Results:The impact of the RNLE has been both widespread and influential. We found 24 studies that examined the criteria used to define lifting capacity used by the RNLE, 28 studies that compared risk assessment methods for identifying LBP, 23 studies that found the RNLE useful in identifying the risk of LBP with different work populations, and 13 studies on the relationship between LI/CLI and LBP outcomes. We also found evidence on the adoption of the RNLE as an ergonomic standard for use by various local, state, and international entities.Conclusion:The review found 13 studies that link LI/CLI to adverse LBP outcomes. These studies showed a positive relationship between LI/CLI metrics and the severity of LBP outcomes.

Objective A computer vision method was developed for estimating the trunk flexion angle, angular speed, and angular acceleration by extracting simple features from the moving image during lifting. Background Trunk kinematics is an important risk factor for lower back pain, but is often difficult to measure by practitioners for lifting risk assessments. Methods Mannequins representing a wide range of hand locations for different lifting postures were systematically generated using the University of Michigan 3DSSPP software. A bounding box was drawn tightly around each mannequin and regression models estimated trunk angles. The estimates were validated against human posture data for 216 lifts collected using a laboratory-grade motion capture system and synchronized video recordings. Trunk kinematics, based on bounding box dimensions drawn around the subjects in the video recordings of the lifts, were modeled for consecutive video frames. Results The mean absolute difference between predicted and motion capture measured trunk angles was 14.7°, and there was a significant linear relationship between predicted and measured trunk angles ( R2 = .80, p < .001). The training error for the kinematics model was 2.3°. Conclusion Using simple computer vision-extracted features, the bounding box method indirectly estimated trunk angle and associated kinematics, albeit with limited precision. Application This computer vision method may be implemented on handheld devices such as smartphones to facilitate automatic lifting risk assessments in the workplace.

Objective: A method for automatically classifying lifting postures from simple features in video recordings was developed and tested. We explored if an "elastic" rectangular bounding box, drawn tightly around the subject, can be used for classifying standing, stooping, and squatting at the lift origin and destination. Background: Current marker-less video tracking methods depend on a priori skeletal human models, which are prone to error from poor illumination, obstructions, and difficulty placing cameras in the field. Robust computer vision algorithms based on spatiotemporal features were previously applied for evaluating repetitive motion tasks, exertion frequency, and duty cycle. Methods: Mannequin poses were systematically generated using the Michigan 3DSSPP software for a wide range of hand locations and lifting postures. The stature-normalized height and width of a bounding box were measured in the sagittal plane and when rotated horizontally by 30°. After randomly ordering the data, a classification and regression tree algorithm was trained to classify the lifting postures. Results: The resulting tree had four levels and four splits, misclassifying 0.36% training-set cases. The algorithm was tested using 30 video clips of industrial lifting tasks, misclassifying 3.33% test-set cases. The sensitivity and specificity, respectively, were 100.0% and 100.0% for squatting, 90.0% and 100.0% for stooping, and 100.0% and 95.0% for standing. Conclusions: The tree classification algorithm is capable of classifying lifting postures based only on dimensions of bounding boxes. Applications: It is anticipated that this practical algorithm can be implemented on handheld devices such as a smartphone, making it readily accessible to practitioners.